Load scripts: loads libraries and useful scripts used in the analyses; all .R files contained in scripts at the root of the factory are automatically loaded
Load data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)These scripts will load:
.R files inside /scripts/.R files inside /src/These scripts also contain routines to access the latest clean encrypted data (see next section).
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 157,722 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 2[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bed… 27 mk454hr East of E…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bla… 9 bb12fd North West
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bro… 11 br33ql London
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_can… 9 ws111jp Midlands
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_cit… 12 n15lz London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_enf… 7 en40dy London
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ham… 6 dl62uu North Eas…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_har… 24 ts232la North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_kin… 6 kt11eu London
## [90m# … with 157,712 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 64
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 101
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 12
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 5
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 0
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 7
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 2
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 5
## 115 2020-06-23 East of England 4
## 116 2020-06-24 East of England 2
## 117 2020-06-25 East of England 0
## 118 2020-03-01 London 0
## 119 2020-03-02 London 0
## 120 2020-03-03 London 0
## 121 2020-03-04 London 0
## 122 2020-03-05 London 0
## 123 2020-03-06 London 1
## 124 2020-03-07 London 0
## 125 2020-03-08 London 0
## 126 2020-03-09 London 1
## 127 2020-03-10 London 0
## 128 2020-03-11 London 6
## 129 2020-03-12 London 6
## 130 2020-03-13 London 10
## 131 2020-03-14 London 14
## 132 2020-03-15 London 10
## 133 2020-03-16 London 15
## 134 2020-03-17 London 23
## 135 2020-03-18 London 27
## 136 2020-03-19 London 25
## 137 2020-03-20 London 44
## 138 2020-03-21 London 49
## 139 2020-03-22 London 54
## 140 2020-03-23 London 63
## 141 2020-03-24 London 87
## 142 2020-03-25 London 113
## 143 2020-03-26 London 129
## 144 2020-03-27 London 130
## 145 2020-03-28 London 122
## 146 2020-03-29 London 146
## 147 2020-03-30 London 149
## 148 2020-03-31 London 181
## 149 2020-04-01 London 202
## 150 2020-04-02 London 191
## 151 2020-04-03 London 196
## 152 2020-04-04 London 230
## 153 2020-04-05 London 195
## 154 2020-04-06 London 197
## 155 2020-04-07 London 220
## 156 2020-04-08 London 238
## 157 2020-04-09 London 206
## 158 2020-04-10 London 170
## 159 2020-04-11 London 178
## 160 2020-04-12 London 158
## 161 2020-04-13 London 166
## 162 2020-04-14 London 144
## 163 2020-04-15 London 142
## 164 2020-04-16 London 139
## 165 2020-04-17 London 100
## 166 2020-04-18 London 101
## 167 2020-04-19 London 103
## 168 2020-04-20 London 95
## 169 2020-04-21 London 94
## 170 2020-04-22 London 109
## 171 2020-04-23 London 77
## 172 2020-04-24 London 71
## 173 2020-04-25 London 58
## 174 2020-04-26 London 53
## 175 2020-04-27 London 51
## 176 2020-04-28 London 43
## 177 2020-04-29 London 44
## 178 2020-04-30 London 40
## 179 2020-05-01 London 41
## 180 2020-05-02 London 41
## 181 2020-05-03 London 36
## 182 2020-05-04 London 30
## 183 2020-05-05 London 25
## 184 2020-05-06 London 37
## 185 2020-05-07 London 37
## 186 2020-05-08 London 30
## 187 2020-05-09 London 23
## 188 2020-05-10 London 26
## 189 2020-05-11 London 18
## 190 2020-05-12 London 18
## 191 2020-05-13 London 16
## 192 2020-05-14 London 20
## 193 2020-05-15 London 18
## 194 2020-05-16 London 14
## 195 2020-05-17 London 15
## 196 2020-05-18 London 9
## 197 2020-05-19 London 14
## 198 2020-05-20 London 19
## 199 2020-05-21 London 12
## 200 2020-05-22 London 10
## 201 2020-05-23 London 6
## 202 2020-05-24 London 7
## 203 2020-05-25 London 9
## 204 2020-05-26 London 12
## 205 2020-05-27 London 7
## 206 2020-05-28 London 8
## 207 2020-05-29 London 7
## 208 2020-05-30 London 12
## 209 2020-05-31 London 6
## 210 2020-06-01 London 10
## 211 2020-06-02 London 7
## 212 2020-06-03 London 6
## 213 2020-06-04 London 8
## 214 2020-06-05 London 4
## 215 2020-06-06 London 0
## 216 2020-06-07 London 4
## 217 2020-06-08 London 5
## 218 2020-06-09 London 4
## 219 2020-06-10 London 7
## 220 2020-06-11 London 5
## 221 2020-06-12 London 3
## 222 2020-06-13 London 3
## 223 2020-06-14 London 2
## 224 2020-06-15 London 1
## 225 2020-06-16 London 2
## 226 2020-06-17 London 1
## 227 2020-06-18 London 2
## 228 2020-06-19 London 3
## 229 2020-06-20 London 3
## 230 2020-06-21 London 4
## 231 2020-06-22 London 2
## 232 2020-06-23 London 0
## 233 2020-06-24 London 2
## 234 2020-06-25 London 1
## 235 2020-03-01 Midlands 0
## 236 2020-03-02 Midlands 0
## 237 2020-03-03 Midlands 1
## 238 2020-03-04 Midlands 0
## 239 2020-03-05 Midlands 0
## 240 2020-03-06 Midlands 0
## 241 2020-03-07 Midlands 0
## 242 2020-03-08 Midlands 3
## 243 2020-03-09 Midlands 1
## 244 2020-03-10 Midlands 0
## 245 2020-03-11 Midlands 2
## 246 2020-03-12 Midlands 6
## 247 2020-03-13 Midlands 5
## 248 2020-03-14 Midlands 4
## 249 2020-03-15 Midlands 5
## 250 2020-03-16 Midlands 11
## 251 2020-03-17 Midlands 8
## 252 2020-03-18 Midlands 13
## 253 2020-03-19 Midlands 8
## 254 2020-03-20 Midlands 28
## 255 2020-03-21 Midlands 13
## 256 2020-03-22 Midlands 31
## 257 2020-03-23 Midlands 33
## 258 2020-03-24 Midlands 41
## 259 2020-03-25 Midlands 48
## 260 2020-03-26 Midlands 64
## 261 2020-03-27 Midlands 72
## 262 2020-03-28 Midlands 89
## 263 2020-03-29 Midlands 92
## 264 2020-03-30 Midlands 90
## 265 2020-03-31 Midlands 123
## 266 2020-04-01 Midlands 140
## 267 2020-04-02 Midlands 142
## 268 2020-04-03 Midlands 124
## 269 2020-04-04 Midlands 151
## 270 2020-04-05 Midlands 164
## 271 2020-04-06 Midlands 140
## 272 2020-04-07 Midlands 123
## 273 2020-04-08 Midlands 186
## 274 2020-04-09 Midlands 139
## 275 2020-04-10 Midlands 127
## 276 2020-04-11 Midlands 142
## 277 2020-04-12 Midlands 139
## 278 2020-04-13 Midlands 120
## 279 2020-04-14 Midlands 116
## 280 2020-04-15 Midlands 147
## 281 2020-04-16 Midlands 102
## 282 2020-04-17 Midlands 118
## 283 2020-04-18 Midlands 115
## 284 2020-04-19 Midlands 92
## 285 2020-04-20 Midlands 107
## 286 2020-04-21 Midlands 86
## 287 2020-04-22 Midlands 78
## 288 2020-04-23 Midlands 103
## 289 2020-04-24 Midlands 79
## 290 2020-04-25 Midlands 72
## 291 2020-04-26 Midlands 81
## 292 2020-04-27 Midlands 74
## 293 2020-04-28 Midlands 68
## 294 2020-04-29 Midlands 53
## 295 2020-04-30 Midlands 56
## 296 2020-05-01 Midlands 64
## 297 2020-05-02 Midlands 51
## 298 2020-05-03 Midlands 52
## 299 2020-05-04 Midlands 61
## 300 2020-05-05 Midlands 58
## 301 2020-05-06 Midlands 59
## 302 2020-05-07 Midlands 48
## 303 2020-05-08 Midlands 34
## 304 2020-05-09 Midlands 37
## 305 2020-05-10 Midlands 42
## 306 2020-05-11 Midlands 33
## 307 2020-05-12 Midlands 45
## 308 2020-05-13 Midlands 40
## 309 2020-05-14 Midlands 37
## 310 2020-05-15 Midlands 40
## 311 2020-05-16 Midlands 34
## 312 2020-05-17 Midlands 31
## 313 2020-05-18 Midlands 34
## 314 2020-05-19 Midlands 34
## 315 2020-05-20 Midlands 36
## 316 2020-05-21 Midlands 32
## 317 2020-05-22 Midlands 27
## 318 2020-05-23 Midlands 34
## 319 2020-05-24 Midlands 19
## 320 2020-05-25 Midlands 26
## 321 2020-05-26 Midlands 33
## 322 2020-05-27 Midlands 29
## 323 2020-05-28 Midlands 28
## 324 2020-05-29 Midlands 20
## 325 2020-05-30 Midlands 20
## 326 2020-05-31 Midlands 22
## 327 2020-06-01 Midlands 20
## 328 2020-06-02 Midlands 22
## 329 2020-06-03 Midlands 24
## 330 2020-06-04 Midlands 15
## 331 2020-06-05 Midlands 21
## 332 2020-06-06 Midlands 20
## 333 2020-06-07 Midlands 17
## 334 2020-06-08 Midlands 15
## 335 2020-06-09 Midlands 18
## 336 2020-06-10 Midlands 15
## 337 2020-06-11 Midlands 13
## 338 2020-06-12 Midlands 12
## 339 2020-06-13 Midlands 6
## 340 2020-06-14 Midlands 17
## 341 2020-06-15 Midlands 12
## 342 2020-06-16 Midlands 14
## 343 2020-06-17 Midlands 10
## 344 2020-06-18 Midlands 14
## 345 2020-06-19 Midlands 9
## 346 2020-06-20 Midlands 13
## 347 2020-06-21 Midlands 12
## 348 2020-06-22 Midlands 12
## 349 2020-06-23 Midlands 12
## 350 2020-06-24 Midlands 11
## 351 2020-06-25 Midlands 3
## 352 2020-03-01 North East and Yorkshire 0
## 353 2020-03-02 North East and Yorkshire 0
## 354 2020-03-03 North East and Yorkshire 0
## 355 2020-03-04 North East and Yorkshire 0
## 356 2020-03-05 North East and Yorkshire 0
## 357 2020-03-06 North East and Yorkshire 0
## 358 2020-03-07 North East and Yorkshire 0
## 359 2020-03-08 North East and Yorkshire 0
## 360 2020-03-09 North East and Yorkshire 0
## 361 2020-03-10 North East and Yorkshire 0
## 362 2020-03-11 North East and Yorkshire 0
## 363 2020-03-12 North East and Yorkshire 0
## 364 2020-03-13 North East and Yorkshire 0
## 365 2020-03-14 North East and Yorkshire 0
## 366 2020-03-15 North East and Yorkshire 2
## 367 2020-03-16 North East and Yorkshire 3
## 368 2020-03-17 North East and Yorkshire 1
## 369 2020-03-18 North East and Yorkshire 2
## 370 2020-03-19 North East and Yorkshire 6
## 371 2020-03-20 North East and Yorkshire 5
## 372 2020-03-21 North East and Yorkshire 6
## 373 2020-03-22 North East and Yorkshire 7
## 374 2020-03-23 North East and Yorkshire 9
## 375 2020-03-24 North East and Yorkshire 8
## 376 2020-03-25 North East and Yorkshire 18
## 377 2020-03-26 North East and Yorkshire 21
## 378 2020-03-27 North East and Yorkshire 28
## 379 2020-03-28 North East and Yorkshire 35
## 380 2020-03-29 North East and Yorkshire 38
## 381 2020-03-30 North East and Yorkshire 64
## 382 2020-03-31 North East and Yorkshire 60
## 383 2020-04-01 North East and Yorkshire 67
## 384 2020-04-02 North East and Yorkshire 74
## 385 2020-04-03 North East and Yorkshire 100
## 386 2020-04-04 North East and Yorkshire 105
## 387 2020-04-05 North East and Yorkshire 92
## 388 2020-04-06 North East and Yorkshire 96
## 389 2020-04-07 North East and Yorkshire 102
## 390 2020-04-08 North East and Yorkshire 107
## 391 2020-04-09 North East and Yorkshire 111
## 392 2020-04-10 North East and Yorkshire 117
## 393 2020-04-11 North East and Yorkshire 98
## 394 2020-04-12 North East and Yorkshire 84
## 395 2020-04-13 North East and Yorkshire 94
## 396 2020-04-14 North East and Yorkshire 107
## 397 2020-04-15 North East and Yorkshire 96
## 398 2020-04-16 North East and Yorkshire 103
## 399 2020-04-17 North East and Yorkshire 88
## 400 2020-04-18 North East and Yorkshire 95
## 401 2020-04-19 North East and Yorkshire 88
## 402 2020-04-20 North East and Yorkshire 100
## 403 2020-04-21 North East and Yorkshire 76
## 404 2020-04-22 North East and Yorkshire 84
## 405 2020-04-23 North East and Yorkshire 63
## 406 2020-04-24 North East and Yorkshire 72
## 407 2020-04-25 North East and Yorkshire 69
## 408 2020-04-26 North East and Yorkshire 65
## 409 2020-04-27 North East and Yorkshire 65
## 410 2020-04-28 North East and Yorkshire 57
## 411 2020-04-29 North East and Yorkshire 69
## 412 2020-04-30 North East and Yorkshire 57
## 413 2020-05-01 North East and Yorkshire 64
## 414 2020-05-02 North East and Yorkshire 48
## 415 2020-05-03 North East and Yorkshire 40
## 416 2020-05-04 North East and Yorkshire 49
## 417 2020-05-05 North East and Yorkshire 40
## 418 2020-05-06 North East and Yorkshire 51
## 419 2020-05-07 North East and Yorkshire 45
## 420 2020-05-08 North East and Yorkshire 42
## 421 2020-05-09 North East and Yorkshire 44
## 422 2020-05-10 North East and Yorkshire 40
## 423 2020-05-11 North East and Yorkshire 29
## 424 2020-05-12 North East and Yorkshire 27
## 425 2020-05-13 North East and Yorkshire 28
## 426 2020-05-14 North East and Yorkshire 31
## 427 2020-05-15 North East and Yorkshire 32
## 428 2020-05-16 North East and Yorkshire 35
## 429 2020-05-17 North East and Yorkshire 26
## 430 2020-05-18 North East and Yorkshire 30
## 431 2020-05-19 North East and Yorkshire 27
## 432 2020-05-20 North East and Yorkshire 22
## 433 2020-05-21 North East and Yorkshire 33
## 434 2020-05-22 North East and Yorkshire 22
## 435 2020-05-23 North East and Yorkshire 18
## 436 2020-05-24 North East and Yorkshire 26
## 437 2020-05-25 North East and Yorkshire 21
## 438 2020-05-26 North East and Yorkshire 21
## 439 2020-05-27 North East and Yorkshire 22
## 440 2020-05-28 North East and Yorkshire 20
## 441 2020-05-29 North East and Yorkshire 25
## 442 2020-05-30 North East and Yorkshire 20
## 443 2020-05-31 North East and Yorkshire 20
## 444 2020-06-01 North East and Yorkshire 16
## 445 2020-06-02 North East and Yorkshire 23
## 446 2020-06-03 North East and Yorkshire 23
## 447 2020-06-04 North East and Yorkshire 17
## 448 2020-06-05 North East and Yorkshire 18
## 449 2020-06-06 North East and Yorkshire 21
## 450 2020-06-07 North East and Yorkshire 14
## 451 2020-06-08 North East and Yorkshire 11
## 452 2020-06-09 North East and Yorkshire 12
## 453 2020-06-10 North East and Yorkshire 18
## 454 2020-06-11 North East and Yorkshire 7
## 455 2020-06-12 North East and Yorkshire 9
## 456 2020-06-13 North East and Yorkshire 10
## 457 2020-06-14 North East and Yorkshire 11
## 458 2020-06-15 North East and Yorkshire 8
## 459 2020-06-16 North East and Yorkshire 10
## 460 2020-06-17 North East and Yorkshire 9
## 461 2020-06-18 North East and Yorkshire 10
## 462 2020-06-19 North East and Yorkshire 6
## 463 2020-06-20 North East and Yorkshire 4
## 464 2020-06-21 North East and Yorkshire 4
## 465 2020-06-22 North East and Yorkshire 6
## 466 2020-06-23 North East and Yorkshire 7
## 467 2020-06-24 North East and Yorkshire 7
## 468 2020-06-25 North East and Yorkshire 1
## 469 2020-03-01 North West 0
## 470 2020-03-02 North West 0
## 471 2020-03-03 North West 0
## 472 2020-03-04 North West 0
## 473 2020-03-05 North West 1
## 474 2020-03-06 North West 0
## 475 2020-03-07 North West 0
## 476 2020-03-08 North West 1
## 477 2020-03-09 North West 0
## 478 2020-03-10 North West 0
## 479 2020-03-11 North West 0
## 480 2020-03-12 North West 2
## 481 2020-03-13 North West 3
## 482 2020-03-14 North West 1
## 483 2020-03-15 North West 4
## 484 2020-03-16 North West 2
## 485 2020-03-17 North West 4
## 486 2020-03-18 North West 6
## 487 2020-03-19 North West 7
## 488 2020-03-20 North West 10
## 489 2020-03-21 North West 11
## 490 2020-03-22 North West 13
## 491 2020-03-23 North West 15
## 492 2020-03-24 North West 21
## 493 2020-03-25 North West 21
## 494 2020-03-26 North West 29
## 495 2020-03-27 North West 35
## 496 2020-03-28 North West 28
## 497 2020-03-29 North West 46
## 498 2020-03-30 North West 67
## 499 2020-03-31 North West 52
## 500 2020-04-01 North West 86
## 501 2020-04-02 North West 96
## 502 2020-04-03 North West 95
## 503 2020-04-04 North West 98
## 504 2020-04-05 North West 102
## 505 2020-04-06 North West 100
## 506 2020-04-07 North West 135
## 507 2020-04-08 North West 127
## 508 2020-04-09 North West 119
## 509 2020-04-10 North West 117
## 510 2020-04-11 North West 138
## 511 2020-04-12 North West 125
## 512 2020-04-13 North West 129
## 513 2020-04-14 North West 131
## 514 2020-04-15 North West 114
## 515 2020-04-16 North West 135
## 516 2020-04-17 North West 98
## 517 2020-04-18 North West 113
## 518 2020-04-19 North West 71
## 519 2020-04-20 North West 83
## 520 2020-04-21 North West 76
## 521 2020-04-22 North West 86
## 522 2020-04-23 North West 85
## 523 2020-04-24 North West 66
## 524 2020-04-25 North West 65
## 525 2020-04-26 North West 55
## 526 2020-04-27 North West 54
## 527 2020-04-28 North West 57
## 528 2020-04-29 North West 62
## 529 2020-04-30 North West 59
## 530 2020-05-01 North West 45
## 531 2020-05-02 North West 56
## 532 2020-05-03 North West 55
## 533 2020-05-04 North West 48
## 534 2020-05-05 North West 48
## 535 2020-05-06 North West 44
## 536 2020-05-07 North West 49
## 537 2020-05-08 North West 42
## 538 2020-05-09 North West 30
## 539 2020-05-10 North West 41
## 540 2020-05-11 North West 35
## 541 2020-05-12 North West 38
## 542 2020-05-13 North West 25
## 543 2020-05-14 North West 26
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## 547 2020-05-18 North West 31
## 548 2020-05-19 North West 35
## 549 2020-05-20 North West 27
## 550 2020-05-21 North West 27
## 551 2020-05-22 North West 26
## 552 2020-05-23 North West 31
## 553 2020-05-24 North West 26
## 554 2020-05-25 North West 31
## 555 2020-05-26 North West 27
## 556 2020-05-27 North West 27
## 557 2020-05-28 North West 28
## 558 2020-05-29 North West 20
## 559 2020-05-30 North West 19
## 560 2020-05-31 North West 13
## 561 2020-06-01 North West 12
## 562 2020-06-02 North West 27
## 563 2020-06-03 North West 22
## 564 2020-06-04 North West 22
## 565 2020-06-05 North West 15
## 566 2020-06-06 North West 25
## 567 2020-06-07 North West 19
## 568 2020-06-08 North West 20
## 569 2020-06-09 North West 15
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## 571 2020-06-11 North West 16
## 572 2020-06-12 North West 10
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## 574 2020-06-14 North West 15
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## 576 2020-06-16 North West 11
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## 578 2020-06-18 North West 11
## 579 2020-06-19 North West 7
## 580 2020-06-20 North West 11
## 581 2020-06-21 North West 6
## 582 2020-06-22 North West 10
## 583 2020-06-23 North West 10
## 584 2020-06-24 North West 10
## 585 2020-06-25 North West 4
## 586 2020-03-01 South East 0
## 587 2020-03-02 South East 0
## 588 2020-03-03 South East 1
## 589 2020-03-04 South East 0
## 590 2020-03-05 South East 1
## 591 2020-03-06 South East 0
## 592 2020-03-07 South East 0
## 593 2020-03-08 South East 1
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## 595 2020-03-10 South East 1
## 596 2020-03-11 South East 1
## 597 2020-03-12 South East 0
## 598 2020-03-13 South East 1
## 599 2020-03-14 South East 1
## 600 2020-03-15 South East 5
## 601 2020-03-16 South East 8
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## 603 2020-03-18 South East 10
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## 605 2020-03-20 South East 13
## 606 2020-03-21 South East 7
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## 611 2020-03-26 South East 35
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## 613 2020-03-28 South East 36
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## 630 2020-04-14 South East 65
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## 645 2020-04-29 South East 47
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## 658 2020-05-12 South East 27
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## 660 2020-05-14 South East 32
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## 693 2020-06-16 South East 10
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## 696 2020-06-19 South East 6
## 697 2020-06-20 South East 4
## 698 2020-06-21 South East 3
## 699 2020-06-22 South East 2
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## 701 2020-06-24 South East 1
## 702 2020-06-25 South East 0
## 703 2020-03-01 South West 0
## 704 2020-03-02 South West 0
## 705 2020-03-03 South West 0
## 706 2020-03-04 South West 0
## 707 2020-03-05 South West 0
## 708 2020-03-06 South West 0
## 709 2020-03-07 South West 0
## 710 2020-03-08 South West 0
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## 712 2020-03-10 South West 0
## 713 2020-03-11 South West 1
## 714 2020-03-12 South West 0
## 715 2020-03-13 South West 0
## 716 2020-03-14 South West 1
## 717 2020-03-15 South West 0
## 718 2020-03-16 South West 0
## 719 2020-03-17 South West 2
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## 734 2020-04-01 South West 22
## 735 2020-04-02 South West 23
## 736 2020-04-03 South West 30
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## 738 2020-04-05 South West 32
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## 745 2020-04-12 South West 23
## 746 2020-04-13 South West 27
## 747 2020-04-14 South West 24
## 748 2020-04-15 South West 32
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## 760 2020-04-27 South West 13
## 761 2020-04-28 South West 17
## 762 2020-04-29 South West 15
## 763 2020-04-30 South West 26
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## 799 2020-06-05 South West 2
## 800 2020-06-06 South West 1
## 801 2020-06-07 South West 3
## 802 2020-06-08 South West 3
## 803 2020-06-09 South West 0
## 804 2020-06-10 South West 0
## 805 2020-06-11 South West 2
## 806 2020-06-12 South West 2
## 807 2020-06-13 South West 2
## 808 2020-06-14 South West 0
## 809 2020-06-15 South West 1
## 810 2020-06-16 South West 1
## 811 2020-06-17 South West 0
## 812 2020-06-18 South West 0
## 813 2020-06-19 South West 0
## 814 2020-06-20 South West 2
## 815 2020-06-21 South West 0
## 816 2020-06-22 South West 1
## 817 2020-06-23 South West 1
## 818 2020-06-24 South West 1
## 819 2020-06-25 South West 0We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Thursday 25 Jun 2020.
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_reportWe plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lagThis analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -10.3406 -2.7238 -0.3202 3.5525 5.7975
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.861e+00 5.431e-02 89.50 <2e-16 ***
## note_lag 1.234e-05 5.523e-07 22.34 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 13.39081)
##
## Null deviance: 7123.80 on 55 degrees of freedom
## Residual deviance: 747.01 on 54 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 129.153136 1.000012
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 115.964169 143.482247
## note_lag 1.000011 1.000013
Rsq(lag_mod)
## [1] 0.8951382
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fitThis is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom")
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_regWe save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))The following information documents the system on which the document was compiled.
This provides information on the operating system.
This provides information on the version of R used:
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.14
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.5.0 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0 ellipsis_0.3.1
## [5] rprojroot_1.3-2 snakecase_0.11.0 fs_1.4.1 rstudioapi_0.11
## [9] farver_2.0.3 fansi_0.4.1 splines_4.0.2 knitr_1.29
## [13] jsonlite_1.7.0 broom_0.5.6 dbplyr_1.4.4 compiler_4.0.2
## [17] httr_1.4.1 backports_1.1.8 assertthat_0.2.1 Matrix_1.2-18
## [21] cli_2.0.2 htmltools_0.5.0 prettyunits_1.1.1 tools_4.0.2
## [25] gtable_0.3.0 glue_1.4.1 Rcpp_1.0.4.6 carData_3.0-4
## [29] cellranger_1.1.0 vctrs_0.3.1 nlme_3.1-148 matchmaker_0.1.1
## [33] crosstalk_1.1.0.1 xfun_0.15 ps_1.3.3 openxlsx_4.1.5
## [37] lifecycle_0.2.0 rstatix_0.6.0 MASS_7.3-51.6 scales_1.1.1
## [41] hms_0.5.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [45] gridExtra_2.3 stringi_1.4.6 kyotil_2019.11-22 boot_1.3-25
## [49] pkgbuild_1.0.8 zip_2.0.4 rlang_0.4.6 pkgconfig_2.0.3
## [53] evaluate_0.14 lattice_0.20-41 labeling_0.3 htmlwidgets_1.5.1
## [57] cowplot_1.0.0 processx_3.4.2 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.4 haven_2.3.1 foreign_0.8-80 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-12 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-8 utf8_1.1.4 rmarkdown_2.3
## [77] viridis_0.5.1 grid_4.0.2 readxl_1.3.1 data.table_1.12.8
## [81] blob_1.2.1 callr_3.4.3 reprex_0.3.0 digest_0.6.25
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0